This document discusses the limitations of traditional database technologies and introduces associative technology as an evolution in database storage and retrieval. Some key limitations of traditional databases include disparate data sources, lack of timely information, high costs, and complex systems. Associative technology models data in an 'n' normal form that maps data relationally like human memory. It stores single instances of data values and uses bidirectional pointers to associate related data. This approach eliminates data redundancy and allows for fast, flexible querying of complex, large datasets.
AtomicDB is a data ingestion and storage tool inspired by human memory. It addresses challenges of heterogeneous and complex cyber-physical system data by storing data as "atoms" in a common associative vector space, rather than in tables. This allows for a unified representation of any data type. Relationships between data atoms are formed based on attributes and stored as tokens, enabling fast retrieval and correlation of semantically related information without additional searching. The tool provides three distinct associative dimensions to model different functional perspectives.
This document introduces a new data modelling approach and compares it to traditional relational database models. It provides definitions and examples of key concepts in relational modelling like entities, attributes, relations, and constraints. It also demonstrates various ways to represent these constructs in Wolfram Language, including as lists, associations, graphs, and RDF triplets. The goal is to help software developers and data modellers learn the advantages of applying this new method.
Effective Data Retrieval in XML using TreeMatch AlgorithmIRJET Journal
This document summarizes research on effective data retrieval from XML documents using the TreeMatch algorithm. It begins with an abstract that introduces the TreeMatch algorithm and its ability to provide fast data retrieval from XML documents by matching tree-shaped patterns. It then reviews related work on XML tree matching algorithms and their issues like suboptimality. The document proposes using the TreeMatch algorithm to overcome issues with wildcards, negation, and siblings when querying XML documents with XPath or XQuery. It provides details on the TreeMatch algorithm and its ability to process different types of XML tree pattern queries efficiently while avoiding intermediate results. In conclusion, it states that the TreeMatch algorithm can efficiently handle three types of XML tree pattern queries and overcome the problem of sub
This document discusses the object oriented data model (OODM). It defines the OODM and describes how it accommodates relationships like aggregation, generalization, and particularization. The OODM provides four types of data operations: defining schemas, creating databases, retrieving objects, and expanding objects. Key features of the OODM include object identity, abstraction, encapsulation, data hiding, inheritance, and classes. The document concludes that a prototype of the OODM has been implemented to model application domains and that menus can be created, accessed, and updated like data from the database schema in the OODM.
- AtomicDB uses a vector space model to represent data as interconnected informational elements at the center of their relationship universes, allowing each data item to act as an entry point into the network.
- Associations in AtomicDB are bidirectional references between data items, with no separate connector or predicate items. The algorithm that determines associations is entirely fact-based.
- Large datasets can be distributed across multiple servers by mapping data element tokens to different physical locations on contingent high-bandwidth networks.
The document compares conceptual, logical, and physical data models. Conceptual models show entities and relationships without attributes or keys. Logical models add attributes, primary keys, and foreign keys. Physical models specify tables, columns, data types, and foreign keys to represent the database implementation. The complexity increases from conceptual to logical to physical models.
Development of a new indexing technique for XML document retrievalAmjad Ali
The document proposes a new indexing technique for XML document retrieval that addresses issues with existing techniques. It represents an XML document as a tree structure with nodes corresponding to elements, attributes, and content. Nodes are labeled with start/end positions and level to allow efficient updates by leaving gaps between labels. The technique permits fast retrieval of ancestor-descendant and parent-child relationships without recomputing the index on updates. Future work could include indexing comments and handling two separate indices for updates and queries.
A collection of conceptual tools for describing
data
data relationships
data semantics
data constraints
Relational model
Entity-Relationship model
Other models:
object-oriented model
semi-structured data models
Older models: network model and hierarchical model
AtomicDB is a data ingestion and storage tool inspired by human memory. It addresses challenges of heterogeneous and complex cyber-physical system data by storing data as "atoms" in a common associative vector space, rather than in tables. This allows for a unified representation of any data type. Relationships between data atoms are formed based on attributes and stored as tokens, enabling fast retrieval and correlation of semantically related information without additional searching. The tool provides three distinct associative dimensions to model different functional perspectives.
This document introduces a new data modelling approach and compares it to traditional relational database models. It provides definitions and examples of key concepts in relational modelling like entities, attributes, relations, and constraints. It also demonstrates various ways to represent these constructs in Wolfram Language, including as lists, associations, graphs, and RDF triplets. The goal is to help software developers and data modellers learn the advantages of applying this new method.
Effective Data Retrieval in XML using TreeMatch AlgorithmIRJET Journal
This document summarizes research on effective data retrieval from XML documents using the TreeMatch algorithm. It begins with an abstract that introduces the TreeMatch algorithm and its ability to provide fast data retrieval from XML documents by matching tree-shaped patterns. It then reviews related work on XML tree matching algorithms and their issues like suboptimality. The document proposes using the TreeMatch algorithm to overcome issues with wildcards, negation, and siblings when querying XML documents with XPath or XQuery. It provides details on the TreeMatch algorithm and its ability to process different types of XML tree pattern queries efficiently while avoiding intermediate results. In conclusion, it states that the TreeMatch algorithm can efficiently handle three types of XML tree pattern queries and overcome the problem of sub
This document discusses the object oriented data model (OODM). It defines the OODM and describes how it accommodates relationships like aggregation, generalization, and particularization. The OODM provides four types of data operations: defining schemas, creating databases, retrieving objects, and expanding objects. Key features of the OODM include object identity, abstraction, encapsulation, data hiding, inheritance, and classes. The document concludes that a prototype of the OODM has been implemented to model application domains and that menus can be created, accessed, and updated like data from the database schema in the OODM.
- AtomicDB uses a vector space model to represent data as interconnected informational elements at the center of their relationship universes, allowing each data item to act as an entry point into the network.
- Associations in AtomicDB are bidirectional references between data items, with no separate connector or predicate items. The algorithm that determines associations is entirely fact-based.
- Large datasets can be distributed across multiple servers by mapping data element tokens to different physical locations on contingent high-bandwidth networks.
The document compares conceptual, logical, and physical data models. Conceptual models show entities and relationships without attributes or keys. Logical models add attributes, primary keys, and foreign keys. Physical models specify tables, columns, data types, and foreign keys to represent the database implementation. The complexity increases from conceptual to logical to physical models.
Development of a new indexing technique for XML document retrievalAmjad Ali
The document proposes a new indexing technique for XML document retrieval that addresses issues with existing techniques. It represents an XML document as a tree structure with nodes corresponding to elements, attributes, and content. Nodes are labeled with start/end positions and level to allow efficient updates by leaving gaps between labels. The technique permits fast retrieval of ancestor-descendant and parent-child relationships without recomputing the index on updates. Future work could include indexing comments and handling two separate indices for updates and queries.
A collection of conceptual tools for describing
data
data relationships
data semantics
data constraints
Relational model
Entity-Relationship model
Other models:
object-oriented model
semi-structured data models
Older models: network model and hierarchical model
An extended database reverse engineering – a key for database forensic invest...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Purpose of the data base system, data abstraction, data model, data independence, data definition
language, data manipulation language, data base manager, data base administrator, data base users,
overall structure.
ER Models, entities, mapping constrains, keys, E-R diagram, reduction E-R diagrams to tables,
generatio, aggregation, design of an E-R data base scheme.
Oracle RDBMS, architecture, kernel, system global area (SGA), data base writer, log writer, process
monitor, archiver, database files, control files, redo log files, oracle utilities.
SQL: commands and data types, data definition language commands, data manipulation commands,
data query language commands, transaction language control commands, data control language
commands.
Joins, equi-joins, non-equi-joins, self joins, other joins, aggregate functions, math functions, string
functions, group by clause, data function and concepts of null values, sub-querries, views.
PL/SQL, basics of pl/sql, data types, control structures, database access with PL/SQL, data base
connections, transaction management, data base locking, cursor management.
The document describes several database models:
- Hierarchical model organizes data in a tree structure and allows records to have repeating information. It was popular from the 1960s-1970s.
- Network model permitted modeling many-to-many relationships and was formally defined in 1971.
- Relational model represents data as tables and allows definition of structures, storage, retrieval and integrity constraints. It is the most commonly implemented model today.
- Object/relational model adds object storage capabilities to the relational model.
Download Complete Material - https://www.instamojo.com/prashanth_ns/
This RDBMS (Relational Database Management System) contains 9 Units and each unit contains 40 to 50 slides in it.
Contents…
• Define a Database Management System
• Describe the types of data models
• Create an entity-relationship model
• List the types of relationships between entities
• Define a Relational Database Management System
• Describe the operators that work on relations
• Identify tips of logical database design
• Map an ER diagram to a table
• Describe data redundancy
• Describe the first, second, and third normal forms
• Describe the Boyce-Codd Normal Form
• Appreciate the need for denormalization.
1) A data model abstracts the essential qualities of a dataset and describes the data in an organization. It involves determining user and application data requirements and integrating them into an overall conceptual view.
2) Conceptual and physical data models are created. The conceptual model specifies data for human understanding while the physical model aids database design.
3) Data modeling defines entities, attributes, relationships and identifies primary keys. It also establishes constraints and referential integrity rules for the data.
This document discusses database design using Entity Relationship Diagrams (ERDs). It covers how to draw ERDs using Chen's Model and Crow's Foot notations and define the basic elements of ERDs. Conversion rules are presented to convert ERDs into relational tables for one-to-one, one-to-many, and many-to-many relationships. An example is given to demonstrate drawing an ERD for a company database and converting it into relational tables.
The document discusses object-oriented databases and the need for complex data types that traditional databases cannot support well. It covers the core concepts of the object-oriented data model including objects, classes, inheritance, and object identity. Key advantages of the object-oriented approach include its ability to model complex relationships and enable persistence of programming language objects.
An Efficient Annotation of Search Results Based on Feature Ranking Approach f...Computer Science Journals
With the increased number of web databases, major part of deep web is one of the bases of database. In several search engines, encoded data in the returned resultant pages from the web often comes from structured databases which are referred as Web databases (WDB).
The document discusses the relational data model and query languages. It provides the following key points:
1. The relational data model organizes data into tables with rows and columns, where rows represent records and columns represent attributes. Relations between data are represented through tables.
2. Relational integrity constraints include key constraints, domain constraints, and referential integrity constraints to ensure valid data.
3. Relational algebra and calculus provide theoretical foundations for query languages like SQL. Relational algebra uses operators like select, project, join on relations, while relational calculus specifies queries using logic.
purpose of database systems, components of dbms, applications of
dbms, three tier dbms architecture, data independence, database schema, instance, data modeling,
entity relationship model, relational model
Schema Integration, View Integration and Database Integration, ER Model & Dia...Mobarok Hossen
What is ER Model & Diagrams?
How can you design ER Model & Diagram?
What is Object-Oriented Model?
What is Schema Integration? how can you Schema Integrate?
What is View Integration? how can you View Integrate?
What is Database Integration? how can you Database Integrate?
HOLISTIC EVALUATION OF XML QUERIES WITH STRUCTURAL PREFERENCES ON AN ANNOTATE...ijseajournal
With the emergence of XML as de facto format for storing and exchanging information over the Internet, the search for ever more innovative and effective techniques for their querying is a major and current concern of the XML database community. Several studies carried out to help solve this problem are mostly oriented towards the evaluation of so-called exact queries which, unfortunately, are likely (especially in the case of semi-structured documents) to yield abundant results (in the case of vague queries) or empty results (in the case of very precise queries). From the observation that users who make requests are not necessarily interested in all possible solutions, but rather in those that are closest to their needs, an important field of research has been opened on the evaluation of preferences queries. In this paper, we propose an approach for the evaluation of such queries, in case the preferences concern the structure of the document. The solution investigated revolves around the proposal of an evaluation plan in three phases: rewriting-evaluation-merge. The rewriting phase makes it possible to obtain, from a partitioningtransformation operation of the initial query, a hierarchical set of preferences path queries which are holistically evaluated in the second phase by an instrumented version of the algorithm TwigStack. The merge phase is the synthesis of the best results.
EFFICIENT SCHEMA BASED KEYWORD SEARCH IN RELATIONAL DATABASESIJCSEIT Journal
Keyword search in relational databases allows user to search information without knowing database
schema and using structural query language (SQL). In this paper, we address the problem of generating
and evaluating candidate networks. In candidate network generation, the overhead is caused by raising the
number of joining tuples for the size of minimal candidate network. To reduce overhead, we propose
candidate network generation algorithms to generate a minimum number of joining tuples according to the
maximum number of tuple set. We first generate a set of joining tuples, candidate networks (CNs). It is
difficult to obtain an optimal query processing plan during generating a number of joins. We also develop a
dynamic CN evaluation algorithm (D_CNEval) to generate connected tuple trees (CTTs) by reducing the
size of intermediate joining results. The performance evaluation of the proposed algorithms is conducted
on IMDB and DBLP datasets and also compared with existing algorithms.
This document discusses denormalization, which refers to modifying a relational schema to be less normalized by combining relations or duplicating attributes. It describes 7 common denormalization techniques: 1) combining one-to-one relations, 2) duplicating attributes in one-to-many relations, 3) duplicating foreign keys, 4) duplicating attributes in many-to-many relations, 5) introducing repeating groups, 6) creating extract tables, and 7) partitioning relations. While denormalization can improve performance, it can also increase complexity, reduce flexibility, and slow down updates. Data integrity must be maintained when denormalizing.
Day by day data is increasing, and most of the data stored in a database after manual transformations and derivations. Scientists can facilitate data intensive applications to study and understand the behaviour of a complex system. In a data intensive application, a scientific model facilitates raw data products to produce new data products and that data is collected from various sources such as physical, geological, environmental, chemical and biological etc. Based on the generated output, it is important to have the ability of tracing an output data product back to its source values if that particular output seems to have an unexpected value. Data provenance helps scientists to investigate the origin of an unexpected value. In this paper our aim is to find a reason behind the unexpected value from a database using query inversion and we are going to propose some hypothesis to make an inverse query for complex aggregation function and multiple relationship (join, set operation) function.
The document discusses key concepts in relational database management systems including:
1) Everything is represented as relations (tables) with attributes (columns) and tuples (rows) that make up the relations.
2) Schemas define the structure of relations with attributes and primary keys to uniquely identify tuples.
3) Relations can be related through foreign keys that match primary keys in other relations.
4) Integrity rules like entity and referential integrity enforce valid relationships between tuples in different relations.
The document discusses key concepts of relational databases and relational algebra. It defines what a relation is as a set of tuples with attributes, and covers attribute types, keys, relations schemas and instances. It also summarizes the core relational algebra operations of selection, projection, join, union, difference and Cartesian product and how they are used to manipulate and query relations.
This white paper proposes a concept called "Data Convergence" to provide a unified view of open government datasets from different sources and formats. The solution would build a software application with an HTTP API to integrate datasets and identify relationships between them based on common attributes. This would allow users to more easily analyze linked datasets and derive useful information. The benefits of this approach include easy access to real-time converged data through standard JSON/XML formats with loose coupling between the underlying data storage and applications.
The document discusses database concepts including:
- What a database is and its components like data, hardware, software, and users.
- Database management systems (DBMS) that enable users to define, create and maintain databases.
- Data models like hierarchical, network, and relational models. Relational databases using SQL are now most common.
- Database design including logical design, physical implementation, and application development.
- Key concepts like data abstraction, instances and schemas, normalization, and integrity rules.
The document provides an overview of the topics covered in a data processing course over 10 weeks. Week 1 covers data models and data types. Week 2 discusses data modelling and the components and importance of data models. Weeks 3 and 4 focus on database normalization, including 1NF, 2NF, 3NF and denormalization. Week 5 introduces the star schema model. Weeks 6-8 cover using Microsoft Access and relational data models. Week 9 reviews file organization techniques. Week 10 is for revision and exams.
An extended database reverse engineering – a key for database forensic invest...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Purpose of the data base system, data abstraction, data model, data independence, data definition
language, data manipulation language, data base manager, data base administrator, data base users,
overall structure.
ER Models, entities, mapping constrains, keys, E-R diagram, reduction E-R diagrams to tables,
generatio, aggregation, design of an E-R data base scheme.
Oracle RDBMS, architecture, kernel, system global area (SGA), data base writer, log writer, process
monitor, archiver, database files, control files, redo log files, oracle utilities.
SQL: commands and data types, data definition language commands, data manipulation commands,
data query language commands, transaction language control commands, data control language
commands.
Joins, equi-joins, non-equi-joins, self joins, other joins, aggregate functions, math functions, string
functions, group by clause, data function and concepts of null values, sub-querries, views.
PL/SQL, basics of pl/sql, data types, control structures, database access with PL/SQL, data base
connections, transaction management, data base locking, cursor management.
The document describes several database models:
- Hierarchical model organizes data in a tree structure and allows records to have repeating information. It was popular from the 1960s-1970s.
- Network model permitted modeling many-to-many relationships and was formally defined in 1971.
- Relational model represents data as tables and allows definition of structures, storage, retrieval and integrity constraints. It is the most commonly implemented model today.
- Object/relational model adds object storage capabilities to the relational model.
Download Complete Material - https://www.instamojo.com/prashanth_ns/
This RDBMS (Relational Database Management System) contains 9 Units and each unit contains 40 to 50 slides in it.
Contents…
• Define a Database Management System
• Describe the types of data models
• Create an entity-relationship model
• List the types of relationships between entities
• Define a Relational Database Management System
• Describe the operators that work on relations
• Identify tips of logical database design
• Map an ER diagram to a table
• Describe data redundancy
• Describe the first, second, and third normal forms
• Describe the Boyce-Codd Normal Form
• Appreciate the need for denormalization.
1) A data model abstracts the essential qualities of a dataset and describes the data in an organization. It involves determining user and application data requirements and integrating them into an overall conceptual view.
2) Conceptual and physical data models are created. The conceptual model specifies data for human understanding while the physical model aids database design.
3) Data modeling defines entities, attributes, relationships and identifies primary keys. It also establishes constraints and referential integrity rules for the data.
This document discusses database design using Entity Relationship Diagrams (ERDs). It covers how to draw ERDs using Chen's Model and Crow's Foot notations and define the basic elements of ERDs. Conversion rules are presented to convert ERDs into relational tables for one-to-one, one-to-many, and many-to-many relationships. An example is given to demonstrate drawing an ERD for a company database and converting it into relational tables.
The document discusses object-oriented databases and the need for complex data types that traditional databases cannot support well. It covers the core concepts of the object-oriented data model including objects, classes, inheritance, and object identity. Key advantages of the object-oriented approach include its ability to model complex relationships and enable persistence of programming language objects.
An Efficient Annotation of Search Results Based on Feature Ranking Approach f...Computer Science Journals
With the increased number of web databases, major part of deep web is one of the bases of database. In several search engines, encoded data in the returned resultant pages from the web often comes from structured databases which are referred as Web databases (WDB).
The document discusses the relational data model and query languages. It provides the following key points:
1. The relational data model organizes data into tables with rows and columns, where rows represent records and columns represent attributes. Relations between data are represented through tables.
2. Relational integrity constraints include key constraints, domain constraints, and referential integrity constraints to ensure valid data.
3. Relational algebra and calculus provide theoretical foundations for query languages like SQL. Relational algebra uses operators like select, project, join on relations, while relational calculus specifies queries using logic.
purpose of database systems, components of dbms, applications of
dbms, three tier dbms architecture, data independence, database schema, instance, data modeling,
entity relationship model, relational model
Schema Integration, View Integration and Database Integration, ER Model & Dia...Mobarok Hossen
What is ER Model & Diagrams?
How can you design ER Model & Diagram?
What is Object-Oriented Model?
What is Schema Integration? how can you Schema Integrate?
What is View Integration? how can you View Integrate?
What is Database Integration? how can you Database Integrate?
HOLISTIC EVALUATION OF XML QUERIES WITH STRUCTURAL PREFERENCES ON AN ANNOTATE...ijseajournal
With the emergence of XML as de facto format for storing and exchanging information over the Internet, the search for ever more innovative and effective techniques for their querying is a major and current concern of the XML database community. Several studies carried out to help solve this problem are mostly oriented towards the evaluation of so-called exact queries which, unfortunately, are likely (especially in the case of semi-structured documents) to yield abundant results (in the case of vague queries) or empty results (in the case of very precise queries). From the observation that users who make requests are not necessarily interested in all possible solutions, but rather in those that are closest to their needs, an important field of research has been opened on the evaluation of preferences queries. In this paper, we propose an approach for the evaluation of such queries, in case the preferences concern the structure of the document. The solution investigated revolves around the proposal of an evaluation plan in three phases: rewriting-evaluation-merge. The rewriting phase makes it possible to obtain, from a partitioningtransformation operation of the initial query, a hierarchical set of preferences path queries which are holistically evaluated in the second phase by an instrumented version of the algorithm TwigStack. The merge phase is the synthesis of the best results.
EFFICIENT SCHEMA BASED KEYWORD SEARCH IN RELATIONAL DATABASESIJCSEIT Journal
Keyword search in relational databases allows user to search information without knowing database
schema and using structural query language (SQL). In this paper, we address the problem of generating
and evaluating candidate networks. In candidate network generation, the overhead is caused by raising the
number of joining tuples for the size of minimal candidate network. To reduce overhead, we propose
candidate network generation algorithms to generate a minimum number of joining tuples according to the
maximum number of tuple set. We first generate a set of joining tuples, candidate networks (CNs). It is
difficult to obtain an optimal query processing plan during generating a number of joins. We also develop a
dynamic CN evaluation algorithm (D_CNEval) to generate connected tuple trees (CTTs) by reducing the
size of intermediate joining results. The performance evaluation of the proposed algorithms is conducted
on IMDB and DBLP datasets and also compared with existing algorithms.
This document discusses denormalization, which refers to modifying a relational schema to be less normalized by combining relations or duplicating attributes. It describes 7 common denormalization techniques: 1) combining one-to-one relations, 2) duplicating attributes in one-to-many relations, 3) duplicating foreign keys, 4) duplicating attributes in many-to-many relations, 5) introducing repeating groups, 6) creating extract tables, and 7) partitioning relations. While denormalization can improve performance, it can also increase complexity, reduce flexibility, and slow down updates. Data integrity must be maintained when denormalizing.
Day by day data is increasing, and most of the data stored in a database after manual transformations and derivations. Scientists can facilitate data intensive applications to study and understand the behaviour of a complex system. In a data intensive application, a scientific model facilitates raw data products to produce new data products and that data is collected from various sources such as physical, geological, environmental, chemical and biological etc. Based on the generated output, it is important to have the ability of tracing an output data product back to its source values if that particular output seems to have an unexpected value. Data provenance helps scientists to investigate the origin of an unexpected value. In this paper our aim is to find a reason behind the unexpected value from a database using query inversion and we are going to propose some hypothesis to make an inverse query for complex aggregation function and multiple relationship (join, set operation) function.
The document discusses key concepts in relational database management systems including:
1) Everything is represented as relations (tables) with attributes (columns) and tuples (rows) that make up the relations.
2) Schemas define the structure of relations with attributes and primary keys to uniquely identify tuples.
3) Relations can be related through foreign keys that match primary keys in other relations.
4) Integrity rules like entity and referential integrity enforce valid relationships between tuples in different relations.
The document discusses key concepts of relational databases and relational algebra. It defines what a relation is as a set of tuples with attributes, and covers attribute types, keys, relations schemas and instances. It also summarizes the core relational algebra operations of selection, projection, join, union, difference and Cartesian product and how they are used to manipulate and query relations.
This white paper proposes a concept called "Data Convergence" to provide a unified view of open government datasets from different sources and formats. The solution would build a software application with an HTTP API to integrate datasets and identify relationships between them based on common attributes. This would allow users to more easily analyze linked datasets and derive useful information. The benefits of this approach include easy access to real-time converged data through standard JSON/XML formats with loose coupling between the underlying data storage and applications.
The document discusses database concepts including:
- What a database is and its components like data, hardware, software, and users.
- Database management systems (DBMS) that enable users to define, create and maintain databases.
- Data models like hierarchical, network, and relational models. Relational databases using SQL are now most common.
- Database design including logical design, physical implementation, and application development.
- Key concepts like data abstraction, instances and schemas, normalization, and integrity rules.
The document provides an overview of the topics covered in a data processing course over 10 weeks. Week 1 covers data models and data types. Week 2 discusses data modelling and the components and importance of data models. Weeks 3 and 4 focus on database normalization, including 1NF, 2NF, 3NF and denormalization. Week 5 introduces the star schema model. Weeks 6-8 cover using Microsoft Access and relational data models. Week 9 reviews file organization techniques. Week 10 is for revision and exams.
The document discusses developing an online reservation system for a hotel to address problems with low guest occupancy. It outlines the rationale and objectives of creating such a system, which include increasing the number of hotel guests, lessening the time consumed during reservation, highly integrating data, and spending less time searching and retrieving information. The proposed system would allow for online reservation, adding, editing, and deleting guest information, prepaid cards, reloading cards, generating guest account numbers, and producing monthly sales reports. The system aims to improve the current manual reservation process using a graphical user interface and database integration.
It 302 computerized accounting (week 2) - sharifahalish sha
Here are some potential ways to represent relational databases other than using tables and relationships:
- Graph databases: Represent data as nodes, edges, and properties. Nodes represent entities, edges represent relationships between entities. Good for highly connected data.
- Document databases: Store data in flexible, JSON-like documents rather than rigid tables. Good for semi-structured or unstructured data.
- Multidimensional databases (OLAP cubes): Represent data in cubes with dimensions and measures. Good for analytical queries involving aggregation and slicing/dicing of data.
- Network/graph databases: Similar to graph databases but focus more on network properties like paths, connectivity etc. Good for social networks, recommendation systems.
-
The document discusses normal forms in database design and compares the Boyce-Codd normal form (BCNF) to third and fourth normal forms. It also covers semantic data modeling, object-oriented databases, and the differences between distributed and centralized databases. Specifically, it explains that BCNF extends third normal form by requiring that every determinant be a candidate key. It also notes that distributed databases allow data to be stored across multiple physical locations for improved performance and availability compared to centralized databases which store all data in one place.
This chapter discusses data design concepts, file processing systems, database systems, and web-based data design. It explains key data design terminology and how to draw entity relationship diagrams to represent relationships between entities. The chapter also covers database models, data storage and access methods, and data control measures to ensure security and integrity.
This document provides an overview of data science and key concepts in data. It defines data science and describes the data value chain, which identifies the main activities in generating value from data: data acquisition, analysis, curation, storage, and usage. It also defines different data types such as structured, unstructured, and semi-structured data. The document discusses characteristics of big data, including the 3Vs of volume, velocity, and variety as well as other characteristics like veracity and variability. Finally, it outlines the typical big data lifecycle of ingesting, persisting, computing/analyzing, and visualizing data.
The document discusses database design, including the goals of database design such as data availability, reliability, currency, consistency and flexibility. It describes the key components of database design - entities, attributes, and relationships. Entities are things about which data is gathered, attributes are properties of entities, and relationships describe how entities relate to each other. The document also covers logical data modeling, normalization, and the three forms of normalization - first, second and third normal form. The goal of normalization is to organize data to eliminate redundancy and inconsistent dependency.
The document discusses database design, including the goals of database design such as data availability, reliability, currency, consistency and flexibility. It describes the key components of database design - entities, attributes, and relationships. Entities are things about which data is gathered, attributes are properties of entities, and relationships describe how entities relate to each other. The document also covers logical data modeling, normalization, and the three forms of normalization - first, second and third normal form. The goal of normalization is to organize data to eliminate redundancy and inconsistent dependency.
In today’s world there is a wide availability of huge amount of data and thus there is a need for turning this
data into useful information which is referred to as knowledge. This demand for knowledge discovery
process has led to the development of many algorithms used to determine the association rules. One of the
major problems faced by these algorithms is generation of candidate sets. The FP-Tree algorithm is one of
the most preferred algorithms for association rule mining because it gives association rules without
generating candidate sets. But in the process of doing so, it generates many CP-trees which decreases its
efficiency. In this research paper, an improvised FP-tree algorithm with a modified header table, along
with a spare table and the MFI algorithm for association rule mining is proposed. This algorithm generates
frequent item sets without using candidate sets and CP-trees.
This document provides an overview of database management systems (DBMS). It discusses the history and purpose of DBMS, different data models including relational, entity-relationship and object-oriented models. It also describes database languages, data storage and querying, transaction management, and database architecture. Key topics covered include the three levels of data abstraction, database schemas and instances, storage managers, query processors, and ensuring integrity through constraints defined in the data definition language.
The document discusses key concepts in data modeling including:
1) The importance of data modeling in creating a logical database that reduces redundancy and enables efficient data retrieval.
2) How business rules are translated into data model components such as entities, attributes, and relationships.
3) The emergence of standard languages like DML and DLL which helped standardize early network data models.
The document discusses database normalization. It introduces the concept and defines normalization as organizing data to minimize duplication by isolating data across multiple tables and defining relationships between them. It also covers the different normal forms (1st, 2nd, 3rd, and Boyce-Codd), when to normalize data, and provides a real-world school data example to demonstrate normalization concepts.
Relational Theory for Budding Einsteins -- LonestarPHP 2016Dave Stokes
This document provides an overview of relational database theory and normalization for developers. It defines key terms like relational databases, logical and physical data models, database schemas, and data normalization. It explains the concepts of first, second, third and Boyce-Codd normal forms and how to normalize data to these forms by removing redundant and unnecessary data through a multi-step process. The goal of normalization is to organize data to minimize duplication and ensure integrity. An example demonstrates normalizing a dog owner database from first to third normal form.
An extended database reverse engineering v a key for database forensic invest...eSAT Journals
Abstract The database forensic investigation plays an important role in the field of computer. The data stored in the database is generally stored in the form of tables. However, it is difficult to extract meaningful data without blueprints of database because the table inside the database has exceedingly complicated relation and the role of the table and field in the table are ambiguous. Proving a computer crime require very complicated processes which are based on digital evidence collection, forensic analysis and investigation process. Current database reverse engineering researches presume that the information regarding semantics of attributes, primary keys, and foreign keys in database tables is complete. However, this may not be the case. Because in a recent database reverse engineering effort to derive a data model from a table-based database system, we find the data content of many attributes are not related to their names at all. Hence database reverse engineering researches is used to extracts the information regarding semantics of attributes, primary keys, and foreign keys, different consistency constraints in database tables. In this paper, different database reverse engineering (DBRE) process such as table relationship analysis and entity relationship analysis are described .We can extracts an extended entity-relationship diagram from a table-based database with little descriptions for the fields in its tables and no description for keys. Also the analysis of the table relationship using database system catalogue, joins of tables, and design of the process extraction for examination of data is described. Data extraction methods will be used for the digital forensics, which more easily acquires digital evidences from databases using table relationship, entity relationship, different joins among the tables etc. By acquiring these techniques it will be possible for the database user to detect database tampering and dishonest manipulation of database. Index Terms: – Foreign key; Table Relationship; DB Forensic; DBRE;
The document discusses data hierarchy and database management system architecture. It explains that data is organized from bits to fields, records, files and databases. It then describes the three levels of DBMS architecture - internal, conceptual, and external levels. The internal level describes how data is physically stored. The conceptual level describes the database design and schema. The external level presents the data to users without them needing to know the underlying structure. It also briefly introduces different data models including relational, network, and hierarchical models.
Concept and example of a semantic solution implemented with SQL views to cooperate with users on queries over structured data with independence from database schema knowledge and technology.
A Survey on Heterogeneous Data Exchange using XmlIRJET Journal
This document summarizes a research paper on heterogeneous data exchange using XML. It discusses how XML has become a standard for data transmission due to its flexibility, extensibility and ability to represent heterogeneous data. The document then reviews related work on XML data exchange and mapping between relational and XML models. It also describes the process of exporting data from a source database to XML, importing XML data by validating, transforming and storing it in the target database, and transmitting data between different servers.
The document discusses database normalization and provides examples to illustrate the concepts of first, second, and third normal forms. It explains that normalization is the process of evaluating and correcting database tables to minimize data redundancy and anomalies. The key steps in normalization include identifying attributes, dependencies between attributes, and creating normalized tables based on those dependencies. An example database for a college will be used to demonstrate converting tables into first, second, and third normal form. Additionally, an example will show when denormalization of a table may be acceptable.
This document provides an overview of data modeling concepts. It discusses the importance of data modeling, the basic building blocks of data models including entities, attributes, and relationships. It also covers different types of data models such as conceptual, logical, and physical models. The document discusses relational and non-relational data models as well as emerging models like object-oriented, XML, and big data models. Business rules and their role in database design are also summarized.
1. An Evolution in Database
Technology
Introduction to Associative
Technology
2. THE CEO PROBLEM
• Too many disparate Databases and data sources
• Lack of timely information & Incorrect information
• Potential Loss of control of the business
• Sky rocketing cost for storage
• Lack of Enterprise reporting
• Lack of knowledge & support capabilities
• Overly complex systems
Contributing issues :
3. STATEMENT OF THE MARKET
“The business's demand for access to the vast resources of big
data gives information managers an opportunity to alter the way
the enterprise uses information. IT leaders must educate their
business counterparts on the challenges while ensuring some
degree of control and coordination so that the big-data opportunity
doesn't become big-data chaos, which may raise compliance risks,
increase costs and create yet more silos.
Today's information management disciplines and technologies are
simply not up to the task of handling all these dynamics.
Information managers must fundamentally rethink their approach
to data by planning for all the dimensions of information
management.”
-Mark Beyer, research vice president at Gartner
4. PYRAMID OF KNOWLEDGE
SQL FILES
Main
Frame
Trade
Feed
Social VideoNOSQL EXCEL
Multiple Data Sources
Information Technology
Computer Science
Hardware Software (Cir. 1971)
Knowledge
Information
Security Level - Critical
Security Level - Important
Security Level - fractured
5. STATE OF THE MARKET
Explosion of data, exponentially increasing monthly
Security & regulatory compliance
Government, banking & healthcare
Need to aggregate, analyze and deliver quickly
Big data is difficult, expensive and time consuming
Data projects are costly and good talent is hard to find
Needs; ease of use, reduce cost and increase analytic
capabilities
Goal; to deliver insight for business units, customers, and
partners
6. THE BEGINNING OF AN EVOLUTION
Babbage Machine
1600s
Adding Machine
1880s
Vacuum
Tube
1950s
Mainframe
1960s
PC
1990s
Paper Ledger
300+ years
1st Normal Form
Flat Text Files
1950s – today
1st Normal Form
RDBMS –
1971 – Today
3rd Normal Form
7. FORMS OF DATA NORMALIZATION
First Normal Form
First normal form [1] deals with the "shape" of a record type
all occurrences of a record type must contain the same number of fields
Second Normal Form (MongoDB, Raven DB)
Second and third normal forms [2, 3, 7] deal with the relationship between non-key and key fields
Third Normal Form (SQL, NO SQL, Hadoop, Casandra , Mumps, etc.)
Third normal form is violated when a non-key field is a fact about another non-key field
Forth Normal Form (None)
a record type should not contain two or more independent multi-valued facts about an entity
the record must satisfy third normal form
Fifth Normal Form (None)
Fourth and fifth normal forms both deal with combinations of multivalued facts
One difference is that the facts dealt with under fifth normal form are not independent
Sixth Normal Form (None)
A relvar R [table] is in sixth normal form (abbreviated 6NF) if and only if it satisfies no nontrivial join dependencies at all
Some authors use the term sixth normal form differently, namely, as a synonym for Domain/key normal
form (DKNF)
(N) Normal Form ( Associative or Vector based)
PARDIGMN Advance in Information Storage and Retrieval
9. ANALOGY OF ASSOCIATION
I
I Love the Yankees
-
Bi-directional
Pointers
addresses
the Yankees--
Dictionary contains 450,000 unique
words
Associations
x0000456
7
x0000456
9
x0000457
0
x0001 x0002 x0003
AtomicDB logical
Record
x0000456
8
Love
11. SINGLE INSTANCE STORAGE
Data projects are costly and good talent is
hard to find
1991
2001
1986
2001
2012
1993
1991
1998
Lawyer
Teacher
Mother
Mother
Musician
Engineer
Lawyer
Chef
Robert
John
Jeff
Bill
Andre
John
Alex
Bill
Jones
Smith
Jones
Heart
Stone
Jefferson
Washingto
n
Smith
Susan
Susan
Amanda
Amanda
Sally
Summer
Deborah
Brittney
12. SINGLE INSTANCE STORAGE
(POINTERS)
Data projects are costly and good talent is
hard to find
1991
1986
2001
2012
1993
1998
Teacher
Mother
Musician
Engineer
Lawyer
Chef
Robert
Jeff
Bill
Andre
John
Alex
Jones
Smith
Heart
Stone
Jefferson
Washingto
n
Susan
Amanda
Sally
Summer
Deborah
Brittney
Information gets contextualized based on associations
Works like the human brain
13. TECHNICAL - MAPPING TABLES TO
Order Item Quantity Qualifiers Order ID
B7784 2 Red PO090311-72
B7196 3 Brown PO090311-72
B7208 4 Red PO090311-72
B7791 3 Green PO090311-72
B7844 1 Orange PO090311-73
B7863 5 Blue PO090311-74
Order ID
PO090311-72
PO090311-72
PO090311-72
PO090311-72
PO090311-73
PO090311-74
Table Name
Mapping 2-D tables to an ‘n’-D Associative Model enables unbounded extension
and evolution of the data sets, and automatically creates uniqueness (4th normal
form) where replicated values are unified, all record relations are automatically
cross-referenced and where their values (or labels) and their associations become
attributes of the tokens representing them. These tokens (shown next as paired
values) are actually four values, representing >1018th
item indexing.
Order Item
B7784
B7196
B7208
B7791
B7844
B7863
Quantity
2
3
4
3
1
5
Qualifiers
Red
Brown
Red
Green
Orange
Blue
Order Item Quantity Qualifiers Order ID 1,0 ORDER ITEM
B77841,3
B77911,4
B72081,2
B71961,1
B78441,5
B78631,6
2,4 3,1 4,1
2,2 3,1 4,1
2,3 3,3 4,1
2,1 3,5 4,2
2,3 3,2 4,1
2,5 3,4 4,3
2,0 QUANTITY
4*2,4
2*2,2
1*2,1
5*2,5
3*2,3
1,3 3,1 4,1
1,4 3,1 4,1
1,3 3,4 4,3
1,5 3,5 4,2
1,1
3,2
4,1
1,4
3,3
3,0 QUALIFIERS
GREEN3,3
BLUE3,4
BROWN3,2
ORANGE3,5
RED3,1
1,1 2,3 4,1
1,4 2,3 4,1
1,6 2,3 4,3
1,5 2,1 4,2
1,2
2,2
4,1
2,4
1,3
4,0 ORDER ID
PO090311-744,3
PO090311-734,2
PO090311-724,1
1,5 2,1 3,5
1,6 2,5 3,4
1,1
2,2
3,1
1,2 1,3 1,4
2,3 2,4
3,2 3,3
Each table is algorithmically analyzed and column names are extracted. Associative
Contexts are auto generated and initially given the column names as attributes.
(These names are often abbreviations whose meaning is known only to the original
developer but in an Associative Model they can be changed later to ‘friendlier’
names without affecting query operations)
Then the data sets in each column are auto-assimilated as members of the
corresponding Contexts and value duplication is removed, fusing replicated data.
A ‘schema’ qualifying or mapping the meaning of the inter-relationships between
Contexts can be utilized as an import filter or policy, constraining the assimilation
and coordinating the nature of each item’s relationship to every other item.
Accordingly, for each item, reference tokens representing each other row related
item are bilaterally added as associative attributes of each item, thus creating a
fully indexed and cross-referenced model where every item is now associatively
connected to each and every other related item, and where that connection is a
logical address that references where each item is in memory or in storage.
14. PROCESSING A REQUEST
Get 1 , 0 / whose .2 , 0 / = “3” AND .3 , 0 / = “Brown” As Answer
3*2,3
1,1
3,2
4,1
1,4
3,3
BROWN3,2
1,1 2,3 4,1
PO090311-72
4,1
1,1
2,2
3,1
1,2 1,3 1,4
2,3 2,4
3,2 3,3
Get Order Item/ whose .Quantity/ = “3” and .Qualifiers/ = “Brown” ...
14
Get Token/ whose .Token/ = “Value” AND .Token/ = “Value” ...
Each and every data set of interest can be found from a generic query that gets
populated at run time, and since everything is interrelated during assimilation,
queries consist only of Boolean vector operations amongst sets of Tokens.
The User visible text is mapped through the Token Space query template.
4*2,4
2*2,2
1*2,1
2,0 QUANTITY
5*2,5
1,3 3,1 4,1
1,4 3,1 4,1
1,3 3,4 4,3
1,5 3,5 4,2
3*2,3
1,1
3,2
4,1
1,4
3,3
B77841,3
B77911,4
B72081,2
B71961,1
1,0 ORDER ITEM
B78441,5
2,4 3,1 4,1
2,2 3,1 4,1
2,3 3,3 4,1
2,1 3,5 4,2
2,3 3,2 4,1
B7863
2,5 3,4 4,3
1,6
1,1 1,4
B7196
2,3 3,2 4,1
GREEN3,3
BLUE3,4
BROWN3,2
3,0 QUALIFIERS
ORANGE3,5
1,1 2,3 4,1
1,4 2,3 4,1
1,6 2,3 4,3
1,5 2,1 4,2
RED3,1
1,2
2,2
4,1
2,4
1,3
BROWN3,2
1,1 2,3 4,11,1
3 , 2
PO090311-744,3
PO090311-734,2
4,0 ORDER ID
1,5 2,1 3,5
1,6 2,5 3,4
PO090311-72
4,1
1,1
2,2
3,1
1,2 1,3 1,4
2,3 2,4
3,2 3,3
3*2,3
1,1
3,2
4,1
1,4
3,3
2 , 31 , 0
This text to token process is enabled algorithmically and directly maps the alpha /
numeric items from the user, through the Context mapping to the related Tokens.
From the users perspective, either graphically or using some ‘query like’ model, a
context of interest is selected, followed by points of reference.
The associative attributes of the items represented by the points of reference
tokens (2,3 and 3,2) are pulled by index from the system storage and filtered by
the Context of interest (1,0), then pooled in a Boolean operation whose result is
the token (1,1) representing the answer to the user’s query.
The result items’ associative attributes can be read to provide access to all
related items in the system storage. Please note: Unlike a table based system
where every record has to be read and compared to the ‘where’ criteria, only the
criteria ‘items’ need to be read from storage & their attributes ‘pooled’ to get the
answer
A comparison of the processing efficiencies of tables vs tokens is revealing:
For the table example of 100 million records,100 million reads were needed and
each of those reads involved compare operations and record copies for matches.
Here, with the same data set, only four reads were required to get the answer.
15. REPRESENTING RESULTS
3*2,3
1,1
3,2
4,1
1,4
3,3
BROWN3,2
1,1 2,3 4,1
PO090311-72
4,1
1,1
2,2
3,1
1,2 1,3 1,4
2,3 2,4
3,2 3,3
Get Order Item/ whose .Quantity/ = 3 AND .Qualifiers/ = “Brown” ... as Answer
Show Answer, .Quantity/, .Qualifiers/, .Order ID/
15
1 , 0
2 , 0
3 , 0
4 , 0
1 , 1
2 , 3
3 , 2
4 , 1
B7196
2,3 3,2 4,1
1,1
ORDER ITEM QUANTITY QUALIFIERS ORDER ID
B7196 3 Brown PO090311-72
Get 1 , 0 / whose .2 , 0 / = 3 and .3 , 0 / = “Brown” As Answer
Show Answer, .2 , 0 /, .3 , 0 /, .4 , 0 /
The ability to access, process and resolve relationships entirely in token space
with all names, labels and values stored as attributes, enables huge advantages
such as multi-lingual, generic user interfaces that populate themselves from the
data sets, cross-referenced and filtered by user and activity profiles.
In the final step involving presenting the answer to the user, the tokens’
namespace attributes are read and substituted for the tokens in the user interface /
report.
Once the result items’ values and names / labels are presented for the user, the
associative attributes of the items are always kept available, enabling browsing
from any represented item to any or all of its associated items.
The result set is maintained programmatically as a collection of tokens, so they,
and all their relations can be mapped and presented using available visualization;
in grids, node graphs, 3-D fly throughs, pie charts and bar graphs, tab delimited
text, or in whatever customized way the user community wants.
All items can be manipulated generically since they all have an identical
embodiment with their data (as attributes) being the only difference
16. HOW IT WORKS
Traditional
Data Base Records
Traditional
Documents
Repositories
1. Traditional repositories
can be decomposed
to a set of information
nodes
2. These are attributed with
indexes of the set of related
information nodes and are
managed as individual items
or objects in an Associative
Model.
3. They are managed as index
and content nodes in a
Virtual Information Layer.
17. DISPARATE SYSTEMS CO-EXISTENCE
500 + RDBMS
Oracle, DB2, MS
SQL, TXT, Media …
Associative
DB
Staging
Connectivity Hub
Application A Application BApplication A
Application
B
Data Cleansing
Data Normalization
Application C
Associative
DB
Reporting
Intranet
Monitoring
Business
Intelligence
Storage, Licences fees, Agility
Production DW/ DB
Bi-Directional
ONE SOURCE
Allows for No Business Risk
18. DATA WAREHOUSE SOLVED!!
Data Warehouse SQL DB Programming objectsMainframe DB Files System
New Associative
Data Warehouse
Easily Integrate Disparate Systems at Minimal
Costs
Bi-Directional
Synchronization
19. SIMPLE ANALOGIES
“It has been estimated that the vocabulary of English includes
roughly one million words” – Merriam Webster. Every word is
made of a tiny universe of only 26 characters.
Relations Database Management Systems (RDBMS) as well as
any 3rd normal form NO-SQL solutions create and manage
artificial structures producing tremendous overhead and misuse
of resources.
Costly operations such as deletes are non existent in AtomicDB
Fastest operation in computer science is a pointer reference
21. THE BRAIN – VECTOR BASED
Courtesy of UC Berkeley
22. ATOMICDB ADVANTAGES
Performance beyond comparison
1000X faster than SQL on READS (supports case sensitivity if needed)
10X ++ faster on WRITES ( in parallel mode)
Little to no support staff
No Tables, No Views, No whitespaces, No duplicates, No Indexes
Significantly reduce costs in hardware and storage
1/3 the DISK SPACE usage
Easy data analysis, quickly extract real value from your data
Associate Anything to Anything and combine data in all conceivable ways
50-75% reduction in development costs, 80% reduction in
development time
Only 6 Instructions in full API, one line of code access to your data, No queries to write
Object oriented design
Aggregating data from heterogeneous systems is now simple
DOD verified Security Model
PARDIGM Shift in Data Storage and Retrieval
24. WHY ATOMICDB?
Performance beyond comparison
Robust and scalable
Low cost
Unique, non-invasive architecture
Easy to implement
Easy to maintain
Complimentary strategy
Reduces costs over other big data systems
Proven by the US military (US NAVY)
Connectivity Factory™ supports over 500 + financial sources &
data standards
25. WHO IS ATOMIC DATABASE CORP?
Founded in 2011, headquartered in New York
Presence in Switzerland, England, Canada, France, UAE, Saudi
Arabia and Australia
Private Company with 18 Permanent Individuals
Experienced Management Team (Citigroup, Pfizer, Merrill, META
Group, BEA, Plumtree, City of NY, AuthenWare)
Outstanding Board of Advisors
R&D, Production and Support 50% in USA, Canada & 50% in
Switzerland
Market Innovator w/ Most Comprehensive Database System
Proven Scalability & Rapid Market Expansion
26. An Evolution in Database
Technology
Thank You, Now Let’s Get to a Live
Demo
Editor's Notes
Standard banking protocols Through C24, Connectivity Factory™ can embed until 40 Data Object Definition (DOD) libraries which fully encapsulate the published syntax and validation rules for over 40 financial messaging standards. Among them: SWIFT MT/MX, DTCC, FpML, SEPA, ISO 20 022, FIX v4 to v5
Applications Temenos T24, Quod Financial, Sungard GL, Sungard Front Arena, Predator, Kondor, Ullink, SAP, Sales Force, IS Academy, AX Dynamics…